Ambiguity resolution with dialogue search history

ABSTRACT

A method comprising recognizing a user utterance including an ambiguity. The method further comprises using a previously-trained code-generation machine to produce, from the user utterance, a data-flow program including a search-history function. The search-history function is configured to select a highest-confidence disambiguating concept from one or more candidate concepts stored in a context-specific dialogue history.

BACKGROUND

Conversational computing interfaces process user utterances and respondby automatically performing actions, such as answering questions,invoking application programming interfaces (APIs), or otherwiseassisting a user based on the user utterances. Many conversationalcomputing interfaces are limited to processing a pre-defined set ofhard-coded templates, which limits the actions that can be performed bythe conversational computing interface.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter. Furthermore,the claimed subject matter is not limited to implementations that solveany or all disadvantages noted in any part of this disclosure.

A method comprises recognizing a user utterance including an ambiguity.The method further comprises using a previously-trained code-generationmachine to produce, from the user utterance, a data-flow programincluding a search-history function. The search-history function isconfigured to select a highest-confidence disambiguating concept fromone or more candidate concepts stored in a context-specific dialoguehistory.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1D show an exemplary data-flow architecture for an automatedassistant.

FIG. 1E shows an exemplary dialogue between a user and an automatedassistant.

FIG. 2 shows a method of processing a user utterance including anambiguity.

FIGS. 3A-3C show exemplary user utterances including ambiguities andcorresponding data-flow programs for processing the exemplary userutterances.

FIG. 4 shows a method of handling errors during the processing of a userutterance.

FIGS. 5A-5F show exemplary user utterances, corresponding data-flowprograms for processing the exemplary user utterances that may result inerrors, and alternative data-flow programs for resolving the errors.

FIG. 6 shows an exemplary computing system.

DETAILED DESCRIPTION

Conversational computing interfaces may be used to interact with usersvia natural language, for example via speech and/or submitted text. Asan example, automated assistants may be used to assist users via naturallanguage interactions. Although the present disclosure uses an automatedassistant as an example conversational computing interface, this exampleis non-limiting and conversational computing interfaces may beimplemented according to the present disclosure for any suitablepurpose, for example, to permit a user to use natural language tointeract with any suitable computer hardware and/or computer software.As such, every reference to an automated assistant in the presentdisclosure applies equally to any other conversational computerinterface or other computing framework configured to respond to speechor text input.

Automated assistants can use natural language processing (NLP)techniques (e.g., machine learning classifiers) to process an input userutterance (e.g., user speech and/or submitted text) in order to performa predefined, hard-coded action related to the input user utterance. Forexample, an automated assistant may support a predefined plurality ofhard-coded templates, where each template has a number of slots that canbe filled to parametrize a hard-coded action. As an example, anautomated assistant may support a pre-defined interaction to invoke anapplication programming interface (API), e.g., to reserve a seat at arestaurant, call a ride-hailing service, or look up the weather.However, although automated assistants may support a plurality ofdifferent predefined actions via the predefined templates, an automatedassistant that only supports predefined actions via templates may not beconfigured to perform more complex or novel behaviors.

The present disclosure is directed to an automated assistant thatprocesses user utterances using data-flow programs in a data-flowprogramming language (e.g., in addition to or instead of usingtemplates). The automated assistant uses a previously-trained codegeneration machine to generate and/or output a data-flow program for auser utterance, wherein the data-flow program uses a plurality ofpre-defined functions to define individual steps for processing the userutterance.

Processing user utterances by using data-flow programs generated by apreviously-trained code generation machine may result in an improveduser experience, improved efficiency of an automated assistant or otherinteractive computer service (e.g., improved storage usage and/orimproved processing time), and/or improved ability for responding todifferent user utterances. As an example, the data-flow programs mayencode a variety of different processing strategies for different userutterances, including performing calculations based on the userutterances, accessing APIs to respond to the user utterances, etc. Thecode generation machine may generate data-flow programs that arespecific to a user utterance being processed, which may enableprocessing the user utterance more efficiently (e.g., without performingirrelevant steps) and with improved user satisfaction (e.g., bygenerating programs that better address requests expressed in the userutterance). Furthermore, a data-flow program that encounters one or moreerrors may be suspended in order to handle the errors with anerror-recovery program, for example with a modified version of thedata-flow program or with an alternative data-flow program. As such, anapproach that uses the previously-trained code generation machine anddata-flow programs may be more robustly applicable in differentsituations where errors may occur, since different types of errors maybe handled by executing error-recovery data-flow programs.

Accordingly, FIG. 1A shows a data-flow architecture for an automatedassistant system 100. Automated assistant system 100 is configured toprocess a user utterance 102 by operating a previously-trainedcode-generation machine 104 configured to output a data-flow program 106for the user utterance 102. Although the present disclosure focuses oninteraction via natural language (e.g., speech and submitted text),conversational computing interfaces such as automated assistant system100 may further permit interaction via any other suitable input methods,for example, via touch screen inputs and/or button presses. Similarly,although the present disclosure focuses on processing a user utterance102, other inputs such as button press events may be processed insimilar fashion. For example, previously-trained code-generation machine104 may be configured to output a data-flow program 106 for one or morebutton press event(s). Previously-trained code generation machine 104may be trained to recognize different kinds of non-linguistic inputevents, e.g., based on a specific button being pressed, timing of thebutton press relative to other input events, etc.

Data-flow program 106 is shown as a graph including a plurality offunction nodes, wherein the function nodes are depicted with inputs andoutputs shown by arrows. The data-flow program is configured to producea return value indicated by the bottom-most arrow. Previously-trainedcode-generation machine 104 is configured to add any of a plurality ofpre-defined functions 110 to the data-flow program based on the userutterance. Each pre-defined function defines one or more individualsteps for processing the user utterance 102. The data-flow program 106is executable to cause the automated assistant to respond to the userutterance, for example by performing any suitable response action. Thepre-defined functions of the data-flow program 106 may be executable tocause the automated assistant to perform any suitable response action,for example, outputting a response as speech and/or text (e.g.,outputting an assistant response 120 as shown in FIG. 1A), invoking anAPI to perform an action using the API (e.g., ordering food from arestaurant, scheduling a ride with a ride-hailing service, scheduling ameeting in a calendar service, placing a phone call). Although thepresent disclosure focuses on examples in which the automated assistantresponds to utterances by outputting an assistant response (e.g., asspeech and/or text), these examples are non-limiting and the pre-definedfunctions and data-flow programs described herein may be configured tocause the automated assistant to respond to utterances in any suitablemanner, for example by performing one or more actions using an APIinstead of or in addition to outputting an assistant response.

The previously-described code-generation machine 104 described hereinmay be used to respond to user utterances in any suitable fashion. Forexample, the previously-described code-generation machine 104 may beconfigured to recognize a user utterance, and produce, from the userutterance, a data-flow program that defines an executable plan forresponding to the user utterance. The resulting data-flow program may beexecuted, for example by an automated assistant, to process the userutterance. In some examples, the data-flow program for responding to theuser utterance may be executed to respond to the user utterance withoutneeding to generate any additional code using the code-generationmachine 104. In other words, code-generation machine 104 is configuredto output a complete plan for processing a user utterance. Alternatelyor additionally, code-generation machine 104 may be configured to outputa data-flow program for responding a user utterance, wherein some or allof the data-flow program is executed before code-generation machine 104is used to generate further code defining a further plan for completingprocessing of the user utterance. Code-generation machine 104 may beused to plan and execute data-flow programs in any suitable fashion,including completing planning before execution and/or interleavingplanning and execution in any suitable fashion.

The previously-trained code-generation machine 104 may be based on anysuitable technology, such as state-of-the-art or future machine learning(ML), artificial intelligence (AI), and/or natural language processing(NLP) technologies. In some examples, the previously-trainedcode-generation machine 104 includes an encoder machine configured toencode the user utterance 102 as a semantic feature (e.g., a vector in asemantic vector space learned by the previously-trained code-generationmachine 104) and a decoder machine configured to decode the semanticfeature by outputting one or more functions from the plurality ofpre-defined functions 110. In some examples, the decoder machine isconfigured to output the one or more functions according to a typedgrammar for composing functions from the plurality of pre-definedfunctions 110, thereby constraining the outputs of the decoder machineto be well-typed, fully-executable data-flow programs. The plurality ofpre-defined, composable functions 110 supports a range of differentautomated assistant behaviors, e.g., invoking APIs, answering questions,looking up user data, and/or utilizing historical context from acontext-specific dialogue history 130 maintained for the automatedassistant. As shown by the dashed arrows in FIG. 1A, thecontext-specific dialogue history 130 may include any suitable aspectsof the dialogue, for example user utterance 102, data-flow program 106,and/or a resulting assistant response 120.

Accordingly, the user utterance 102, data-flow program 106 generated forthe user utterance 102, and/or any relevant assistant response 120 bythe automated assistant may be stored in the context-specific dialoguehistory 130. Accordingly, the context-specific dialogue history 130defines a plurality of concepts (e.g., concept 130A, and any suitablenumber of concepts including concept 130N). “Concept” is used herein torefer to any relevant or potentially relevant aspects of the interactionbetween the user and the automated assistant. For example, a concept mayinclude an entity (e.g., a person, place, thing, number, date), anintent of a user query (e.g., an intent to order food, an intent to lookup the weather, an intent to schedule a meeting), an action performed bythe automated assistant (e.g., ordering food, looking up the weather,invoking an API, looking up information pertaining to an entity,recognizing a specific user utterance, performing a composite actioncomposed of more than one action), or any other suitable feature. Aconcept may be defined in any suitable manner, for example based on textcontent of the user utterance 102. In some examples, a concept 130A isdefined in terms of a data-flow program fragment 132A. For example, thedata-flow program fragment 132A may include one or more functionsconfigured to look up information pertaining to a specific entity,and/or one or more functions configured to cause the automated assistantto perform a specific action.

In some examples, the plurality of pre-defined functions 110 includesone or more history-accessing functions configured to access thecontext-specific dialogue history 130. Accordingly, data-flow program106 may include such history-accessing functions. For example, theplurality of pre-defined functions 110 and data-flow program 106 eachinclude a history-accessing function 112 configured to access thecontext-specific dialogue history 130 as shown by the arrow. In someexamples, the plurality of pre-defined functions 110 includes asearch-history function configured to look up a concept from thecontext-specific dialogue history 130 (e.g., a getSalient( ) functionconfigured to look up an entity that was previously discussed, or tolook up an action that was previously performed), as will be discussedfurther with regard to FIGS. 1B, 2, and 3A-3F. In some examples, theplurality of pre-defined functions 110 includes a program-rewritingfunction parametrized by a designated concept stored in thecontext-specific dialogue history and configured to generate a newdata-flow program fragment related to the designated concept based onconcepts from context-specific dialogue history 130 (e.g., a Clobber( )function configured to generate a new, re-written program based on thedesignated concept, for example, to write a new program for performing aprevious action with regard to a new entity, or for performing a newaction with regard to a previously-discussed entity). In some examples,the designated concept stored in the context-specific dialogue historyincludes a target sub-concept, and the program-rewriting function isfurther parametrized by a replacing sub-concept for replacing the targetsub-concept. Accordingly, the new data-flow program fragment correspondsto the designated concept with the target sub-concept being replaced bythe replacing sub-concept. In particular, in some examples, the newdata-flow program fragment includes a rewritten data-flow programfragment, based on a historical data-flow program fragment correspondingto the designated concept, wherein a sub-program fragment correspondingto the target sub-concept is replaced by a different sub-programfragment corresponding to the replacing sub-concept. Theprogram-rewriting function will be discussed further with regard toFIGS. 1C, 2, and 3F-3G.

By looking up and/or rewriting concepts from context-specific dialoguehistory 130, the automated assistant may be able to repeat actions,perform actions with modifications, look up relevant entities or otherdetails related to previous actions, etc. For example, the automatedassistant may repeat an action or perform an action with modifications,by re-executing code based on a program fragment 132A corresponding to aconcept 130A from the context-specific dialogue history 130.Furthermore, if any error condition is reached during execution ofdata-flow program 106 prior to outputting an assistant response 120, thedata-flow program 106 and/or other program fragments stored incontext-specific dialogue history 130 may be re-executed (e.g., with orwithout modifications) to recover from the error, as will be describedfurther with regards to FIGS. 1D, 4, and 5A-511.

Accordingly, FIG. 1B shows a different view of automated assistantsystem 100 focusing on a search-history function 112 of the plurality ofpre-defined functions 110. The search-history function 112 is configuredto process an ambiguous user utterance 102 by resolving any ambiguitiesusing concepts from context-specific dialogue history 130. As shown bythe dashed arrow leading from context-specific dialogue history 130 tosearch-history function 112, search-history function 112 is configuredto access one or more concepts from the context-specific dialoguehistory 130 to determine a disambiguating concept 134.

In some examples, disambiguating concept 134 is defined by a programfragment 136. As an example, if user utterance 102 refers to anambiguous entity (e.g., by a pronoun or partial name, such as “Tom”),search-history function 112 may be configured to search for theambiguous entity (e.g., based on the partial name “Tom”) in thecontext-specific dialogue history 130 to find a clarifying entity thatmatches the ambiguous entity (e.g., an entity having a first name “Tom”or a related name such as “Thomas”). In some examples, the clarifyingentity may be defined by a full name (e.g., “Thomas Jones”). In otherexamples, the clarifying entity may be defined by code configured tofind the clarifying entity (e.g., code to lookup people named “Tom” inan address book of the user).

In some examples, disambiguating concept 134 indicates a bridginganaphora between a user utterance and a previous user utterance from thecontext-specific dialogue history. As an example, if a user asks, “whenis my lunch with Charles?” and then subsequently asks, “how long will ittake to get there?” the word “there” in the subsequent user utterancerefers to the location of the lunch with Charles. Accordingly,search-history function 112 may be configured to search for a conceptcorresponding to the location of the lunch with Charles. For example,the concept may include a data-flow program fragment that recursivelyuses the search-history function 112 to find a salient event, namely themeeting with Charles, as well as further instructions to obtain thelocation of the salient event. More generally, concepts found by thesearch-history function 112 may include data-flow programs thatrecursively invoke the search-history function 112 to find any suitablesub-concepts, e.g., so that a salient concept is defined in terms ofsearching for other salient sub-concepts.

The search-history function 112 will be described further with regardsto FIGS. 2 and 3A-3C.

FIG. 1C shows another different view of automated assistant system 100focusing on a program-rewriting function 112′. Program-rewritingfunction 112′ is configured to produce a rewritten concept 140 (e.g.,defined by a rewritten program fragment 142) by starting with adesignated concept 152 from the context-specific dialogue history 130,wherein the designated concept 152 includes at least one replacementtarget sub-concept 154 to be modified/replaced with a different,replacing sub-concept 156. As an example, designated concept 152 mayrefer to an action by the automated assistant including “placing areservation at a local sushi restaurant”. Accordingly, targetsub-concept 154 may be the description “sushi restaurant,” and thereplacing sub-concept 156 may be an alternate description “burritorestaurant.” In particular, designated concept 152 may be defined by aprogram fragment for invoking an API to place restaurant reservations,wherein the API is parametrized by a program fragment corresponding totarget sub-concept 154 that indicates a restaurant at which to placereservations. Accordingly, program-rewriting function 112′ is configuredto output a re-written concept 140 that corresponds to “placing areservation at the local burrito restaurant,” so that re-written concept140 may be used to perform a new action consisting of placing thereservation at the local burrito restaurant (e.g., by executing programfragment 142). The program-rewriting function 112′ will be describedfurther with regards to FIGS. 2 and 3C.

FIG. 1D shows another view of automated assistant system 100 focusing onhandling errors that may arise during execution of data-flow program106. Although not shown in FIG. 1D, as in FIGS. 1A-1C, the userutterance 102, data-flow program 106 and/or assistant response 120 maybe stored into context-specific dialogue history 130 while processinguser utterances.

If an error condition is reached during execution of data-flow program106 prior to outputting an assistant response 120, then the data-flowprogram may be suspended and saved as suspended execution 160.Error-handling execution machine 170 is configured to recover from theerror condition in order to produce the assistant response 120′. Forexample, error-handling execution machine 170 may implement method 400,which is described with regard to FIG. 4, below. To recover from theerror condition, error-handling execution machine 170 may modify and/orre-execute the suspended execution 160. Alternately or additionally,error-handling execution machine 170 may execute an alternate programfragment 180. For example, error-handling execution machine 170 maymodify the suspended execution 160 by using a program-rewriting functionto replace a program fragment of the data-flow program 106 with thealternate program fragment 180, and/or by executing the alternateprogram fragment instead of the data-flow program 106. The alternateprogram fragment 180 may be derived from the context-specific dialoguehistory 130 (e.g., the alternate program fragment 180 may be apreviously-executed program fragment), built from the plurality ofpre-defined functions 110 and/or output by the previously-trainedcode-generation machine 104. In some examples, previously-trainedcode-generation machine 104 is configured to recognize the errorcondition and to output a new program fragment configured to recoverfrom the error. For example, previously-trained code-generation machine104 may be trained with regard to one or more training examples, eachtraining example including an exemplary error condition, and anexemplary data-flow program fragment for responding to the error. Forexample, previously-trained code-generation machine 104 may be trainedwith regard to a large plurality of annotated dialogue histories (aswill be described below), where some or all of the annotated dialoguehistories include an occurrence of an error condition.

In some examples, the error condition may arise due to an ambiguity inthe user utterance 102 in which insufficient information is provided bythe user utterance 102 to fully serve the user based on the userutterance. As an example, if the user utterance is “Schedule a meetingwith Tom” but there is more than one “Tom” in the user's address book,it may not be clear with whom to schedule the meeting. Accordingly, insome examples, error-handling execution machine 170 is configured toexecute code to produce an initial assistant response 120′ including aclarifying question 122. The clarifying question 122 is output for theuser to respond to in a new, clarifying user utterance 102. Accordingly,the clarifying user utterance can be processed using thepreviously-trained code-generation machine 104 to produce a newdata-flow program 106 for responding to the clarifying user utterance.Error recovery, including clarifying questions, is discussed furtherbelow with regard to FIGS. 4 and 5A-5H. The previously-trainedcode-generation machine 104 may be trained via supervised training on aplurality of annotated dialogue histories. In many examples, thepreviously-trained code-generation machine 104 is trained on a largeplurality of annotated dialogue histories (e.g., hundreds, thousands,tens of thousands or more). Annotated dialogue histories describe stateinformation associated with a user interacting with an automatedassistant, annotated with an exemplary data-flow program for respondingto the user interaction. For example, an annotated dialogue history mayinclude a context-specific dialogue history according to the presentdisclosure (as described with regard to FIGS. 3A-3C and 5A-5F below),annotated with an exemplary data-flow program suitable for execution bythe automated assistant in response to the context established in thecontext-specific dialogue history. In examples, a context-specificdialogue history includes a plurality of events arranged in a temporalorder (e.g., time-stamped events), including user utterances, data-flowprograms executed by an automated assistant, responses output by theautomated assistant, and/or error conditions reached while executingdata-flow programs.

As a non-limiting example, an annotated dialogue history may include acontext-specific dialogue history in which the most recent event is anexemplary user utterance, annotated with an exemplary data-flow programfor responding to the exemplary user utterance with regard to thecontext established by the context-specific dialogue history.Accordingly, the previously-trained code-generation machine 104 may betrained to reproduce the exemplary data-flow program given the exemplaryuser utterance and the context-specific dialogue history. The exemplarydata-flow program may include any suitable functions in any suitablesequence/arrangement, so that via training, the code-generation machineis configured to output suitable functions in a suitable sequence. Forexample, the exemplary data-flow program may include the search-historyfunction and accordingly, the code-generation machine may be trained tooutput the search-history function along with other functions in asuitable sequence for responding to a user utterance (e.g., to cause anautomated assistant to perform any suitable response action, such asoutputting a response as text and/or speech, or invoking an API). Insome examples, an annotated dialogue history includes a context-specificdialogue history in which the most recent event is the occurrence of anerror condition (e.g., instead of a user utterance being the most recentevent), annotated with a data-flow program for recovering from theerror. Accordingly, the previously-trained code-generation machine 104may be trained with regard to such annotated dialogue histories togenerate suitable data-flow programs for recovering from errorconditions.

Annotated dialogue histories may be obtained in any suitable manner, forexample, from human demonstrators. For example, one or more humandemonstrators may be shown context-specific dialogue histories (e.g.,context-specific dialogue histories derived from usage data obtainedfrom interaction with humans, and/or machine-generated context-specificdialogue histories), and for each context-specific dialogue history, beasked to provide a suitable data-flow program for responding to thecontext-specific dialogue history. The data-flow programs provided bythe human demonstrators may use the pre-defined functions in anysuitable manner to perform a wide range of tasks responsive to userutterances and/or error conditions.

For example, based on being shown an exemplary user utterance or anexemplary error condition, the human demonstrators may provide exemplarydata-flow programs that perform any suitable calculations, outputresponses (e.g., to ask clarifying questions of the user, or answer aquery issued by the user), listen for utterances from a user (e.g., toobtain clarifications from the user), invoke APIs, etc. Furthermore, theexemplary data-flow programs may include the search-history function(e.g., “getSalient( )”) and/or program-rewriting function (e.g.,“Clobber( )”), invoked with any suitable parameters, for example, toexecute data-flow program fragments from the context-specific dialoguehistory. Accordingly, by training on a plurality of annotated dialoguehistories, the code-generation machine 104 may be trained to generatedata-flow programs similar to those provided by the human demonstrators,in order to respond to user utterances and/or recover from errors.

The data-flow programs (e.g., data-flow programs generated by thepreviously-trained code generation machine and/or exemplary data-flowprograms) are built from a plurality of pre-defined, composablefunctions. The pre-defined, composable functions may be composed intoprograms that invoke the pre-defined, composable functions in in anysuitable order and parametrize the pre-defined, composable functions inany suitable fashion. Accordingly, the previously-trainedcode-generation machine may be trained to output suitable data-flowprograms for a user utterance, based on the exemplary data-flow programsprovided during training. The previously-trained code-generation machineis not limited to hard-coded behaviors. For example, instead of or inaddition to responding to exemplary user utterances seen duringtraining, the previously-trained code-generation machine is configuredto process novel user utterances (that may not have been provided duringtraining) by generating corresponding, novel data-flow programs (thatalso may not have been provided during training).

In order to generalize from specific training examples seen duringtraining and respond to novel user utterances with novel data-flowprograms, the previously-trained code-generation machine may be trainedin any suitable fashion (e.g., using any suitable ML, AI, and/or NLPmodels as will be described below with regard to FIG. 6, on any suitabletraining data, for example, a large plurality of annotated dialoguehistories). In some examples, the previously-trained code-generationmachine may be trained with regard to a loss function for assessingwhether a data-flow program is a suitable response to a user utterance,wherein the loss function is configured to indicate zero loss or arelatively small loss (e.g., requiring no adjustment, or a relativelysmaller adjustment of training parameters) when the code-generationmachine successfully reproduces a training example (e.g., by producingthe same data-flow program as was provided by a human annotator, given acontext-specific dialogue history). However, although the loss functionmay be configured for a relatively small loss when the code-generationmachine successfully reproduces a training example, the loss functionmay also be configured for a relatively small loss when thecode-generation machine reproduces a different data-flow program, forexample, a data-flow program that has a similar effect when executed,and/or a data-flow program that is indicated to be satisfactory by ahuman user (e.g., a human demonstrator and/or end-user of an automatedassistant device). These exemplary approaches for generalization duringtraining are non-limiting, and any suitable AI, ML, and/or NLPtechniques may be utilized to suitably train a code-generation machineto generate suitable data-flow programs in response to a wide variety ofuser utterances.

In some examples, error conditions may be recognized by operating apreviously-trained error detection model. For example, thepreviously-trained error detection model may be trained via supervisedtraining on a plurality of annotated dialogue histories, wherein theannotated dialogue histories are annotated to indicate when errorsoccur. For example, the annotated dialogue histories may be obtained byshowing context-specific dialogue histories to one or more humandemonstrators and asking the human demonstrators to indicate when thecontext-specific dialogue histories indicate an erroneous state.

In addition to history-accessing functions such as a search-historyfunction and a program-rewriting function, the plurality of pre-definedcomposable functions may include any suitable functions, for example, alisten function configured to listen for a specific user utterancebefore continuing with execution of a data-flow program, a responsefunction configured to output a description of a value determined duringexecution of a data-flow program, and/or a primitive calculationfunction for processing values obtained from user utterances and/orcomputed during execution of the data-flow program (e.g., data structureoperations such as forming tuples from data, or arithmetic operations).

In some examples, the plurality of pre-defined composable functionsincludes a foreign function configured to invoke a foreign (i.e., thirdparty) API. For example, foreign APIs may be invoked to interact withreal-world services (e.g., schedule a car in a ride-hailing service,order food or make reservation at a restaurant). In some examples, theplurality of pre-defined composable functions includes an inferencefunction configured to perform a calculation with regard to a result ofa foreign function. The inference function may encapsulate high-levelbehaviors with regard to the API, which would otherwise require multipledifferent low-level functions using the API. For example, a foreignride-hailing API may support functions for scheduling a car, adding astop to a route, and finalizing a scheduled route. Accordingly, aninference function may be configured to receive a destination and, basedon the destination, schedule a car, add a stop corresponding to a pickuplocation for the user, add a stop corresponding to the destination, andfinalize the scheduled route including the stops corresponding to thepickup location and destination. By using inference functions toencapsulate high-level behaviors, the code-generation machine may beable to readily output well-typed code for performing the high-levelbehaviors using a foreign API, without needing to output individualsteps using low-level functions of the foreign API. In some examples, aninference function may be defined with regard to one or more constraintsand executing the inference function may include running a constraintsatisfaction program to satisfy the one or more constraints, beforeinvoking a foreign API using parameters defined by the solution for theconstraints. In some examples, the one or more constraints may include“fuzzy” or “soft” constraints and accordingly, solving the constraintsmay include executing an inference program suitable for “fuzzy” logicalinference, for example a Markov logic inference program.

In some examples, the plurality of pre-defined composable functionsincludes a user-customized function that is configured to access a usercustomization setting and to perform a calculation based on the usercustomization setting. For example, the user-customized function may beconfigured to determine whether the user is free based on auser-customized schedule, e.g., calendar data. User-customized functionsmay be implemented using foreign functions configured to invoke aforeign API, e.g., an API for looking up calendar data.

In some examples, the plurality of pre-defined composable functionsincludes an intelligent decision function, wherein the intelligentdecision function is configured to use a previously-trained machinelearning model to perform a calculation. As an example, thesearch-history function may be an intelligent decision functionconfigured to use a previously-trained relevance detection machine tosearch the context-specific dialogue history. As another example, theplurality of pre-defined composable functions may include an intelligentdecision function configured to assess whether it is “morning” in auser-specific and/or population-specific way, e.g., the function may beconfigured to recognize that what time the user considers “morning”could vary depending on the day of the week or time of the year. Forexample, the intelligent decision function may be configured to assessthat it is “morning” if it is between 6 AM and 11 AM on a weekday, butmay be configured to assess that it is morning if it is between 9 AM and12 PM on a weekend. The intelligent decision function may be trained inany suitable fashion, for example based on labeled examples of times andwhether or not a user considers the time morning. As withuser-customized functions, the intelligent decision function may takeinto account auxiliary information such as a work schedule, calendar,and/or mobile phone usage of a user, for example to determine if it is“morning” based on whether the user has likely woken up yet on a givenday. In some examples, an intelligent decision function may beconfigured to assess an ambiguity (e.g., an ambiguous user utterance, oran ambiguous constraint) and select a disambiguating data-flow programto respond to the ambiguity.

In some examples, the plurality of pre-defined composable functionsincludes a macro function, wherein the macro function includes aplurality of other pre-defined composable functions, and is configuredto execute the plurality of other pre-defined composable functions. Forexample, a macro function can be used to sequence and organize relatedlow-level steps of a high-level task using low-level functions. By usingmacro functions to encapsulate high-level behaviors, the code-generationmachine may be able to readily output well-typed code for performing thehigh-level behaviors without needing to output individual steps usinglow-level functions.

Turning briefly to FIG. 1E, FIG. 1E shows a first example of a dialoguebetween a user and an automated assistant, in which the automatedassistant processes a user utterance 102′ in which there is noambiguity. Context-specific dialogue history 130 is shown includingconcepts 130A,130B, and further concepts through 130N. However, in thisexample, the user utterance 102′ is unambiguous and may be processedwithout referring to the context-specific dialogue history 130. The userasks, “When is my next meeting with Tom Jones?” and thepreviously-trained code-generation machine outputs a data-flow program106′.

Data-flow program 106′ is shown with a non-limiting example syntax inwhich square brackets indicate return values of expressions, e.g.,[events] indicates a return value of an expression for finding allevents matching a set of criteria, and [time] indicates a start time ofthe first such event in the set. The example syntax includes variousfunctions including the search-history function (e.g., “getSalient( )”)and program-rewriting function (e.g., “Clobber( )”) as well as otherfunctions (such as primitive functions, API functions, etc., asdescribed herein). The exemplary functions are shown in a function-callsyntax indicating the name of a function (e.g., “Find”, “getSalient”,“Clobber”, among other named functions) and parentheses enclosingparameters for invoking the function. The exemplary function-call syntaxis non-limiting, and functions may be invoked and parametrized in anysuitable manner (e.g., using any suitable formal-language syntax). Theexemplary function names represent pre-defined functions withimplementations that are not shown herein. For example, each pre-definedfunction may be implemented by any suitable sequence of one or moreinstructions executable by the automated assistant to perform anysuitable steps (e.g., to result in behavior indicated by the functionname, behavior set forth in this disclosure, and/or any other suitablebehavior such as performing calculations, invoking APIs, outputtingaudio, visually presenting information via a display, etc.). Forexample, the “Find” function may be implemented in any suitable manner,for example, by invoking an API to look up information stored in a usercalendar.

As shown, the data-flow program finds events that have an attendee named“Tom Jones.” The data-flow program computes values such as [events] and[time] and then outputs a response 120′ using a “describe” functionconfigured to output a description of the value [time], e.g., as speechvia a speaker of an automated assistant device. Accordingly, response120′ indicates the meeting time for the next meeting with Tom Jones,namely “at 12:30”.

FIG. 2 shows an exemplary method 200 for processing a user utterancethat includes an ambiguity. The method resolves the ambiguity withinformation in a context-specific dialogue history using asearch-history function. As shown in FIG. 3A, the context-specificdialogue history may include any suitable information tracked for adialogue, such as a concept 334 indicating a previous user utterance304, and a concept 324 indicating a previously-executed data-flowprogram fragment 326.

At 202, method 200 includes recognizing the user utterance including theambiguity. For example, FIG. 3A shows an exemplary ambiguous userutterance 302 in which a user asks, “What's after that?”. The utterance,by itself, does not give sufficient information to respond to the user,e.g., because “that” does not refer to any particular event withoutfurther context.

In some examples, at 204, method 200 includes recognizing a constraintrelated to the ambiguity. For example, returning to FIG. 3A, theutterance includes the words “after that,” and accordingly method 200includes recognizing a constraint that the user is referring to arelevant time. For example, the previously-trained code generationmachine may be trained on one or more training examples in which a userutterance includes time-related words such as “after” when the user isreferring to a time. For example, the time may be a start time definedby a schedule for a scheduled event, such as an event scheduled in theuser's calendar.

Accordingly, returning briefly to FIG. 2, at 206, method 200 includesusing the previously-trained code-generation machine to produce adata-flow program for responding to the user utterance, wherein thedata-flow program includes the search-history function, which is called“getSalient” in the exemplary syntax shown in FIG. 3A. Thesearch-history function may be executed to lookup relevant informationfrom the context-specific dialogue history in order to resolve theambiguity. As described at 208, the search-history function isconfigured to select a highest-confidence disambiguating concept fromone or more concepts in the context-specific dialogue history, in orderto use that highest-confidence disambiguating concept to resolve theambiguity.

For example, in FIG. 3A, after the user talked about the meeting withTom Jones, context-specific dialogue history 130 includes a concept 334including a previous user utterance 304 in which the user asked “When ismy next meeting with Tom Jones?” and a concept 324 defined by adata-flow program fragment 326 that was previously executed (e.g.,data-flow program 106′ as shown in FIG. 1E) in response to the previoususer utterance 304. The data-flow program 322 is configured to determinea particular event (e.g., the next meeting with Tom), determine a starttime for the event, and describe the time. Accordingly, thesearch-history function “getSalient” is configured to search for arelevant time from the context-specific dialogue history, in order touse the relevant time for finding events occurring after that time andto output response 330 indicating what events occur after that time. Therelevant time may be the [time] value defined in data-flow program 326pertaining to the meeting with Tom. Accordingly, the response 330indicates that after the meeting with Tom, there is another meeting withRichard Brown.

Returning to FIG. 2, in some examples, as described at 210, thesearch-history function is configured to search, in the context-specificdialogue history, for a subset of one or more candidate concepts thatsatisfy the constraint related to the ambiguity, wherein thehighest-confidence disambiguating concept is found within that subset.For example, in FIG. 3A, based on the utterance 302 including the words“after that,” the recognized constraint may include the constraint thatthe user is referring to a relevant time defined by the schedule for ascheduled event (as described above), so relevant concepts from thecontext-specific dialogue history 130 should be “time” type values.Accordingly, the search-history function “getSalient(Time( ))” isconfigured to search for a subset of relevant values having a “time”type from the context-specific dialogue history 130, e.g., including the[time] value.

In some examples, as described at 212, the search-history function isconfigured to use a previously-trained relevance detection machine torecognize the constraint of the ambiguity, and based on such recognizedconstraint, to select a disambiguating data-flow program fragmentcorresponding to the disambiguating concept. For example, as shown inFIG. 3A, the data-flow program fragment 326 may be selected to use fordisambiguation, based on the “time” type constraint. Thepreviously-trained relevance detection machine may include any suitablecombination of state-of-the-art and/or future ML, AI, and/or NLPtechnologies.

In some examples, the previously-trained relevance detection machine maybe trained via supervised training on a plurality of annotated dialoguehistories, wherein an annotated dialogue history includes an unresolvedsearch-history function labeled with a disambiguating concept that wouldresolve the unresolved search-history function. Accordingly, thepreviously-trained relevance detection machine may be trained to predicta suitable disambiguating concept, given an unresolved search-historyfunction. For example, an annotated dialogue history may include adata-flow program fragment that uses the search-history function (e.g.,data-flow program 322 including “getSalient”) and an exemplarydisambiguating concept that would resolve the ambiguity (e.g., concept324 including data-flow program fragment 326 which defines[time]=[events][0].start).

In some examples, annotated dialogue histories may be provided by humandemonstrators, who may be shown an ambiguous user utterance and acorresponding data-flow program including the search-history function,and asked to provide an exemplary data-flow program fragment that is anappropriate match for the search-history function in the context of theambiguous user utterance. In some examples, the disambiguating conceptthat would resolve the unresolved search-history function is selected bya human demonstrator from the context-specific dialogue history. In someexamples, the disambiguating concept is an exemplary data-flow programfragment received from the human demonstrator. For example, the humandemonstrator may be asked to provide the data-flow program fragment bycomposing one or more of the pre-defined composable functions and/or oneor more data-flow program fragments from the context-specific dialoguehistory. For example, the human demonstrator may be asked to use agraphical user interface (GUI) to select one or more disambiguatingconcepts from the context-specific dialogue history, and/or to composenew programs using such concepts and pre-defined composable functionsselectable from a menu. In some examples, the disambiguating concept isunrelated to the context-specific dialogue history, for example, a humandemonstrator may indicate that a salient date for resolving asearch-history function constrained to look for dates is “today,”regardless of concepts in the context-specific dialogue history.

After producing the data-flow program including the search-historyfunction, at 214 method 200 optionally further includes executing thedata-flow program. Accordingly, at 216 method 200 optionally furtherincludes outputting a response resulting from executing the data-flowprogram. For example, as shown in FIG. 3A, the data-flow program 322 maybe executed, including executing the search-history function to searchfor a salient “time” type value from the history to determine the value[time2], and to find events starting after [time2] (e.g., by invoking anAPI to access the user's calendar). Accordingly, a response 330 may beoutput, to tell the user that “After the meeting with Tom, you have ameeting with Richard Brown, starting at 1:30.”

FIGS. 3B-3C show further examples of resolving user utterances includingambiguities. As shown in FIG. 3B, in some examples, a constraint for theambiguity indicates an ambiguous entity and an entity property of theambiguous entity, and the subset of the one or more candidate conceptsincludes only those candidate entities from the context-specificdialogue history having the entity property. As an example, in userutterance 302′, the user asks to “schedule a meeting with him for 2:30.”The descriptor “him” may be ambiguous without further context, since itcould refer to any person who uses the male pronoun “him.” However,context-specific dialogue history 130 includes potentially relevantentities including Tom Jones indicated by concept 350A and Jane Smithindicated by concept 350B. As described above (and not shown in detailhere), the concept 350A and concept 350B may be represented in anysuitable form, e.g., concept 350A may correspond to a data-flow programfragment for looking up a person named “Tom Jones” by name using an APIfor accessing the user's address book. Based on the user utterance, thedata-flow program 322′ is configured to find a salient person based onthe property of having a male pronoun (“him”). Accordingly, if thehistory includes relevant entities “Tom Jones” and “Jane Smith,” and“Tom Jones” uses male pronouns whereas “Jane Smith” uses femalepronouns, then the search-history function (“getSalient”) may find themore relevant entity “Tom Jones.” Accordingly, response 360 indicatesthat the meeting was scheduled for 2:30 and Tom Jones is being invited.

In some examples, as shown in FIG. 3C, the constraint indicates anambiguous reference to an action performed by the automated assistantand a constraining property related to the action. Accordingly, thesubset of the one or more candidate concepts includes a plurality ofcandidate actions having the constraining property, wherein eachcandidate action is defined by a candidate data-flow program fragment.For example, user utterance 302″ indicates that the user wishes toinvite “Jane” instead, but it is ambiguous from the user utterance 302″what event to invite Jane to. Accordingly, the constraining propertyrelated to the action is that the action is related to inviting a personto an event. Accordingly, there may be a relevant action performed bythe automated assistant, stored as a concept 306 in the context-specificdialogue history 130. As shown, the concept 306 includes a data-flowprogram 308 corresponding to actions performed by the assistant, e.g.,the code corresponding to the meeting set up in the interaction shown inFIG. 3B. The data-flow program 308 for resolving the ambiguity isconfigured to use the search-history function (“getSalient”) to searchfor a relevant event.

In some examples, a concept indicated by a user in a user utterance maybe indicated based on extrinsic and/or situational properties, and/orbased on the provenance of the concept within a conversation between theuser and the automated assistant. As an example, a user utterance mayrefer to a meeting event by referring to “the second meeting,” which mayrefer to a meeting via extrinsic and/or situational properties, forexample the second meeting on the user's schedule. Alternately oradditionally, “the second meeting” may refer to the second meetingdiscussed in the conversation between the user and the automatedassistant. The code-generation machine may be configured, as a result ofthe supervised training herein, to appropriately recognize from contextwhat a user refers to using phrases such as, “the second meeting.”

In some examples, also as shown in FIG. 3C, the data-flow program 322″includes a program-rewriting function (named “Clobber” in the exemplarysyntax) parametrized by a designated concept (“[designated]”) stored inthe context-specific dialogue history and configured to generate a newdata-flow program fragment related to the designated concept based onconcepts from context-specific dialogue history 130 (e.g., to write anew program for performing a previous action with regard to a newentity). In some examples, the designated concept (“[designated]”,namely the scheduling of the new meeting with Tom Jones) includes atarget sub-concept (“replacementTarget”, namely the invitee of theevent, Tom Jones). Accordingly, the program-rewriting function isfurther parametrized by a replacing sub-concept for replacing the targetsub-concept, namely “[replacing]” indicating the new invitee for themeeting, Jane. Accordingly, the new data-flow program fragmentcorresponds to the designated concept with the target sub-concept beingreplaced by the replacing sub-concept. In other words, theprogram-rewriting function results in a new data-flow program configuredto invite Jane Smith to the same event that was previously set up withTom Jones. In particular, in some examples, the new data-flow programfragment includes a rewritten data-flow program fragment, based on ahistorical data-flow program fragment corresponding to the designatedconcept, wherein a sub-program fragment corresponding to the targetsub-concept is replaced by a different sub-program fragmentcorresponding to the replacing sub-concept.

The program-rewriting function may be parametrized in any suitablefashion. For example, the designated concept, replacement targetsub-concept, and replacing sub-concept may be derived from thecontext-specific dialogue history 130 (e.g., the alternate programfragment 180 may be a previously-executed program fragment), built fromthe plurality of pre-defined functions 110 and/or output by thepreviously-trained code-generation machine 104. For example,previously-trained code-generation machine 104 may be trained withregard to one or more training examples including an exemplaryprogram-rewriting function, and an exemplary data-flow program fragmentfor each parameter of the program-rewriting function. In some examples,previously-trained code-generation machine is trained with regard to alarge plurality of different training examples includingprogram-rewriting functions.

In the examples above, the user utterances were processed using thecontext-specific dialogue history, resulting in outputting an automatedassistant response. However, in some examples, an error may occur duringprocessing of a user utterance. Nevertheless, data-flow programsaccording to the present disclosure may be configured for unconditionalevaluation of one or more data values including a return value.Accordingly, processing a data-flow program may include executing thedata-flow program in order to obtain the return value. However, althoughthe data-flow programs may be configured for unconditional evaluation ofthe data values including the return value, errors may occur duringprocessing of the data-flow program, for example due to an ambiguity ina user utterance that precludes fully resolving a data-flow programbased on the user utterance. Accordingly, responsive to detecting anyerror condition while executing the data-flow program, execution of thedata-flow program may be suspended. After suspending execution of thedata-flow program, the previously-trained code generation machine may beused to generate an alternate, error-handling data-flow program based onthe suspended execution of the data-flow program. Accordingly, thealternate, error-handling data-flow program may be executed in order torecover from the error condition.

FIG. 4 shows an exemplary method 400 for processing a user utterance byan automated assistant, when an error may occur during such processing.At 402, method 400 includes recognizing a user utterance for processing.For example, FIG. 5A shows an exemplary dialogue including userutterance 502 in which the user asks, “Who is Dan's manager?”. As inprevious examples, while processing the user utterance the automatedassistant maintains a context-specific dialogue history 130, which isused to track concepts related to previous interactions between the userand the automated assistant, e.g., concepts 540A, 540B, and 540C whichare not shown in detail in the present example.

At 404, method 400 includes using a previously-trained code-generationmachine to generate, from the user utterance, a data-flow program (e.g.,data-flow program 522 shown in FIG. 5A). The data-flow program isconfigured to produce a return value upon successful execution, e.g., toreturn a description of Dan's manager. Accordingly, the data-flowprogram includes computing a first value [r1] which is configured as alist of all of the relevant people with a name like “Dan,” where thelist of results is expected to be a singleton list including exactly oneentry, e.g., a unique result for people with a name like “Dan.” Assumingthe single person named “Dan” is found and saved as [r1], the data-flowprogram further includes computing another value [r2] by finding themanager of [r1], and describing the result.

At 405, method 400 includes executing the data-flow program.Accordingly, at 406, method 400 includes beginning execution of thedata-flow program. Returning briefly to FIG. 5A, evaluating [r1]includes searching for a person named “Dan,” for example by accessingcontext-specific dialogue history 130 and/or accessing an address bookof the user. However, when evaluating [r1], an error condition 532 isreached because there is more than one person with a name like “Dan,”namely “Dan A” and “Dan B,” so the list of people with a name like “Dan”is not a singleton. Accordingly, this discrepancy is detected as theerror condition 532 which includes a data-flow program fragmentdescribing an event in which the error condition is detected. The errorevent indicates that the error is due to ambiguity.

At 408, responsive to reaching an error condition resulting fromexecution of the data-flow program, method 400 includes handling theerror condition. Handling the error condition includes, at 410, prior tothe data-flow program producing the return value, suspending executionof the data-flow program (e.g., data-flow program 522 as shown in FIG.5A), before computing and describing the return value (e.g., returnvalue [r2]). In order to resolve the error condition (e.g., errorcondition 532), the automated assistant is configured to describe theerror, e.g., so as to obtain disambiguating information from the user.Accordingly, optionally at 418, handling the error condition may includeoutputting a clarifying question and recognizing a clarifying userutterance, e.g., by looping back to 402 to recognize a further userutterance. As shown in FIG. 5A, the automated assistant outputs aresponse 534 including a clarifying question, “Did you mean Dan A or DanB?”

Turning now to FIG. 5B, after suspending execution of the data-flowprogram and outputting the response including the clarifying question,the automated assistant may receive a subsequent user utterance toobtain disambiguating information for handling the error. Accordingly,the erroneous data-flow program 522 and the error condition 532 aresaved as concept 520 and concept 530 (respectively) in thecontext-specific dialogue history, while a new user utterance 502′indicating “Dan A” is processed.

Accordingly, at 412, method 400 further includes using thepreviously-trained code-generation machine to generate an error-handlingdata-flow program, wherein the error-handling data-flow program 552 isconfigured to produce the return value (e.g., the error-handlingdata-flow program 552 is an alternate means of computing the expectedvalue corresponding to [r2]).

In some examples, reaching the error condition includes detecting anerror when executing a problematic program fragment of the data-flowprogram, for example, the error condition shown in FIG. 5A arises whenevaluating the problematic program fragment defining the [r1] value,namely “Find(Person(name: like(“Dan”))).results.singleton( )”.Accordingly, generating the error-handing data-flow program includesoutputting a new data-flow program based on the problematic programfragment [r1]. As shown in the example, error-handling data-flow program552 uses the search-history function (“getSalient”) to find a relevantperson named “Dan A,” saving the result as [r3]. In some examples,data-flow programs may include an “intensionOf” function configured toreturn an “intension,” which refers herein to a reference to a data-flowprogram fragment. The error-handling data-flow program 552 uses thedata-flow program fragment corresponding to the [error] event, namely“intensionOf[r1]” as defined in the data-flow program fragment of errorcondition 532.

As shown in FIG. 5B, the new data-flow program based on the problematicprogram fragment includes a program-rewriting function (e.g., “Clobber”)configured to replace, in the data-flow program, the problematic programfragment with an alternate program fragment. The error-handling dataflow program 552 defines a new computation [newComp1] by using theprogram-rewriting function (“Clobber”) to rewrite the original returnvalue [r2] by replacing a target sub-concept defined by [r4], namely theproblematic program fragment that resulted in the error, with adifferent sub-concept [r3].

In some examples, as shown in FIG. 5B, the alternate program fragmentincludes a search-history function configured to search, in thecontext-specific dialogue history, for the alternate program fragmentbased on a relationship to the problematic program fragment.Accordingly, the sub-concept [r3] is defined by code for finding arelevant person named “Dan A” using the search-history function.

At 414, method 400 includes beginning execution of the error-handlingdata-flow program to produce a return value. For example, theerror-handling data-flow program 522 is configured to return value [r2′]by executing the new computation [newComp1]. At 420, method 400 includesoutputting the return value. After outputting a return value, at 422,method 400 further includes outputting a response based on the returnvalue, e.g., FIG. 5B shows a response 514 saying that “Dan's manager isTom Jones.”

In some examples, execution of a data-flow program may lead to reachingmore than one different error condition. For example, after reaching andfully resolving a first error condition, another error condition may bereached. Alternately or additionally, the automated assistant may beconfigured to detect more than one simultaneous error condition andhandle any such error conditions simultaneously by generating anerror-handling data-flow program with regard to all such errorconditions.

In some examples, method 400 includes recognizing a further error duringexecution of the error-handling data-flow program, and executing anerror-recovery loop including producing and executing furthererror-handling data-flow programs, until one of the furthererror-handling data-flow programs produces the return value.Accordingly, handling an error condition responsive to reaching theerror condition at 408 includes, responsive to detecting a further errorcondition at 416, returning to 408 to handle such further errorcondition. Any number of errors may be resolved sequentially and/orsimultaneously by detecting each error in turn, and generating anerror-handling data-flow program to recover from the error(s).

FIG. 5C shows an example where two different error conditions arehandled. FIG. 5C is shown as a series of user utterances and responses,and the context-specific dialogue history and data-flow programspertaining to the utterances/responses are not shown. In the first userutterance 504, the user asks to schedule a meeting with Dan and Tom.However, there may be more than one relevant Dan and similarly, morethan one relevant Tom. Accordingly, in a manner similar to shown inFIGS. 5A-5B, the automated assistant is configured to output a response534′ asking which Dan the user wants to schedule a meeting with. Theuser responds in a user utterance 506 that they meant “Dan A.” Theautomated assistant is configured to output a subsequent response 536asking which Tom the user wants to schedule a meeting with. The userresponds in a user utterance 508 that they meant “Tom J.” Accordingly,after “Dan” and “Tom” are disambiguated, the automated assistant isconfigured to schedule the meeting and to output a response 538indicating that the meeting has been scheduled.

In some examples, as shown in FIG. 5D, responsive to a user utterance510, the data-flow program generated by the previously-trained codegeneration machine designates a concept for resolution using asearch-history function, namely finding a person [r1] by searching for asalient match for “him” mentioned in user utterance 510. However, thesearch-history function may find two or more relevant concepts, asindicated by the error conditions 534″. For example, context-specificdialogue history 130 may include a concept 530A corresponding to “Dan A”and another concept 530B corresponding to “Tom J.” For example, theconcept corresponding to “Dan A” may be defined by a data-flow programfragment such as the fragment “[r2′]=Execute([newComp1])” defined in theerror-recovery data-flow program shown in FIG. 5B, or by any othersuitable data-flow program fragment such as“[r3]=getSalient(Person(name: like(“Dan A”)))”, and the conceptcorresponding to “Tom J” may be defined similarly. Accordingly, theautomated assistant is configured to output a response 536″ including aclarifying question asking the user to provide a clarifying responseindicating one of the two or more relevant concepts (e.g., namely toindicate “Dan A” or “Tom J”). The error-handling data-flow program isfurther configured to receive a clarifying user utterance 512, and toresolve the ambiguity based on the clarifying utterance. Accordingly,after resolving that the user was referring to “Dan A” in the originaluser utterance 510, the automated assistant is configured to perform theaction [r2] to dial the phone number for “Dan A” and to output aresponse 538″ describing this action.

In some examples, as shown in FIG. 5E, the error is an ambiguity whereinthe data-flow program designates a concept for resolution using asearch-history function, the search-history function finds zero relevantconcepts, and the error-handling data-flow program is executable tooutput a clarifying question asking the user to provide a clarifyingresponse indicating a relevant concept, to receive a clarifying userutterance, and to resolve the ambiguity based on the clarifyingutterance. For example, in FIG. 5E the user asks in user utterance 524to “call him to confirm the meeting.” A resulting data-flow program 526is executable to find a relevant person and call that person. However,the context-specific dialogue history 130 may not contain any conceptcorresponding to a relevant person (e.g., because no male people havebeen discussed recently). Accordingly, error condition 534′″ indicatesthat no match is found for finding a salient person to compute [r1].Accordingly, the automated assistant is configured to execute anerror-handling data-flow program (not shown), which includes getting aclarifying response 582 to figure out who to call and calling thatperson (e.g., by using the program-rewriting function to create a newcomputation based on [r1] and/or [r2], similar to the example shown inFIGS. 5A-5B). After the user clarifies that they meant “Dan A” in userutterance 516, the error-handling data-flow program 552 is configured todial “Dan A”'s phone number in order to complete the same steps as theoriginal data-flow program 522, with regard to the clarificationprovided by the user. Accordingly, the data-flow program is configuredto output a response 584 indicating that the call is being dialed.

In some examples, as shown in FIG. 5F, the error is a lack ofconfirmation wherein the data-flow program designates an action to beperformed only after receiving a user confirmation response and the userutterance does not include such user confirmation response, and theerror-handling data-flow program is executable to output a confirmationquestion asking the user to provide the user confirmation responsebefore performing the action. For example, the user asks in userutterance 518 to schedule a ride to the meeting. The data-flow program562 is able to find a salient meeting and determine that there is a rideavailable to get to the meeting now, storing a resulting value in [r2].For example, [r2] may include any suitable information related toscheduling the ride, such as a resource descriptor issued by aride-hailing service API. The data-flow program 562 is configured toschedule a ride based on the resulting value, but only after gettingconfirmation from the user. The lack of confirmation may be detected asan error condition 554. Accordingly, an error-handling data-flow program(not shown) may be configured to output a response 556 indicating a costof the ride and asking the user for confirmation. In user utterance 520,the user indicates that they do want to schedule the ride. Accordingly,the error-handling data-flow program may proceed to schedule the ride,for example, by re-executing the computation [r3], and to output aresponse 558 indicating that the action was performed.

In some examples, the error-handling mechanism described herein may beutilized to determine information that is needed for handling a userutterance, by asking the user one or more follow-up questions. Forexample, if a user utterance includes a request to perform a certainaction that is parametrized by one or more constraints, and the userutterance specifies some, but not all of the constraint values forspecifying the constraints, the action may not be performed beforespecifying the remaining constraints. Accordingly, the missingconstraint parameters may be detected as an error and accordingly, thepreviously-trained code-generation machine is configured to output anerror-handling data-flow program. For example, the error-handling dataflow program may be executable to ask questions to direct the user toprovide further information for specifying the remaining constraints,and to perform the requested action using such further informationreceived from the user. Alternately or additionally, the error-handlingdata flow program may be executable to specify one or more of theremaining constraints with a default value. As an example, if a userasks to “Schedule a meeting with Charles next Thursday at 10 AM,” aresulting data-flow program may be configured to perform ameeting-scheduling that is parametrized by the date, time, invitees, andduration. Since the user utterance mentions “Charles,” “Thursday,” and“10 AM,” the constraints for the date, time, and invitees may bespecified. However, the constraint for the duration may be unspecified,resulting in an error condition. Accordingly, the previously-trainedcode generation machine may be configured to output an error-handlingdata-flow program to specify the remaining constraint for the duration.The error-handling data-flow program may be configured to ask the user afollow-up question, “how long should the meeting be?” in order to getanother user utterance from which the meeting duration may bedetermined. Alternately, the error-handling data-flow program may beconfigured to determine a default value for specifying the constraint,for example, a default meeting duration of 30 minutes. Default valuesmay be determined by the code-generation machine based on supervisedtraining on exemplary default values, by using the search-historyfunction, using any other intelligent-decision function, using auser-customized function (e.g., to access the user's default meetingduration preference), or in any other suitable manner. In some examples,an action defined by a data-flow program is implemented using an APIinference function, which may be parametrized by one or moreconstraints. Accordingly, if any such parameters are not defined by auser utterance, some or all of the parameters may be inferred by runninga constraint satisfaction program. For example, for an API inferencefunction for scheduling meetings that requires a meeting start time anda meeting end time, the meeting end time may be inferred based on themeeting start time and duration. Similarly, the meeting start time maybe inferred based on the meeting end time and duration. Accordingly,error-handling data-flow programs may be configured to infer as manyconstraints as possible for API inference functions, before askingquestions, assuming default values, or otherwise handling missingconstraints. In some examples, instead of generating a single defaultvalue for specifying a constraint, an error-handling data flow programmay be configured to generate a plurality of different candidate valuesfor specifying the constraint and ask the user to select a particularcandidate value. In some examples, the error-handling data flow programmay use the plurality of different candidate values to parametrize andexecute a plurality of different API calls (e.g., using API inferencefunctions) and ask the user to select a candidate value based on resultsof the API calls. For example, the error-handling data flow program mayattempt to schedule multiple different meetings with different durationsand ask the user to select one of the resulting schedules. In someexamples, the error-handling data flow program may filter the candidateoptions based on results of the API calls, e.g., attempting to schedulemultiple different meetings with different durations and asking the userto choose between meetings that do not result in a scheduling conflict,while omitting candidates with a duration that would result in ascheduling conflict as indicated by a meeting-scheduling API.

In some examples, the alternate program fragment is output by thepreviously-trained code-generation machine. Accordingly, thepreviously-trained code-generation machine may be trained to output thealternate program fragment with supervised training based on a pluralityof training examples, wherein a training example includes an exemplaryproblematic program fragment that would cause an error condition, and aresolving alternate program fragment that would not cause the errorcondition. In other words, the previously-trained code generationmachine may be trained in similar fashion with regard to generating codefor a user utterance, as well as with regard to generating code torespond to an error. In either case, the previously-trained codegeneration machine is configured to generate programs using a pluralityof pre-defined, composable functions, arranged in any suitable sequencefor processing the user utterance and/or error. In some examples, thepreviously-trained code-generation machine is trained with regard to alarge plurality of training examples in which an error condition wasreached and in which an alternate program fragment is executable torecover from the error, for example by successfully yielding a returnvalue.

The methods and processes described herein may be tied to a computingsystem of one or more computing devices. In particular, such methods andprocesses may be implemented as an executable computer-applicationprogram, a network-accessible computing service, anapplication-programming interface (API), a library, or a combination ofthe above and/or other compute resources.

FIG. 6 schematically shows a simplified representation of a computingsystem 600 configured to provide any to all of the compute functionalitydescribed herein. Computing system 600 may take the form of one or morepersonal computers, network-accessible server computers, tabletcomputers, home-entertainment computers, gaming devices, mobilecomputing devices, mobile communication devices (e.g., smart phone),virtual/augmented/mixed reality computing devices, wearable computingdevices, Internet of Things (IoT) devices, embedded computing devices,and/or other computing devices.

Computing system 600 includes a logic subsystem 602 and a storagesubsystem 604. Computing system 600 may optionally include aninput/output subsystem 606, communication subsystem 608, and/or othersubsystems not shown in FIG. 6.

Logic subsystem 602 includes one or more physical devices configured toexecute instructions. For example, the logic subsystem may be configuredto execute instructions that are part of one or more applications,services, or other logical constructs. The logic subsystem may includeone or more hardware processors configured to execute softwareinstructions. Additionally or alternatively, the logic subsystem mayinclude one or more hardware or firmware devices configured to executehardware or firmware instructions. Processors of the logic subsystem maybe single-core or multi-core, and the instructions executed thereon maybe configured for sequential, parallel, and/or distributed processing.Individual components of the logic subsystem optionally may bedistributed among two or more separate devices, which may be remotelylocated and/or configured for coordinated processing. Aspects of thelogic subsystem may be virtualized and executed by remotely-accessible,networked computing devices configured in a cloud-computingconfiguration.

Storage subsystem 604 includes one or more physical devices configuredto temporarily and/or permanently hold computer information such as dataand instructions executable by the logic subsystem. When the storagesubsystem includes two or more devices, the devices may be collocatedand/or remotely located. Storage subsystem 604 may include volatile,nonvolatile, dynamic, static, read/write, read-only, random-access,sequential-access, location-addressable, file-addressable, and/orcontent-addressable devices. Storage subsystem 604 may include removableand/or built-in devices. When the logic subsystem executes instructions,the state of storage subsystem 604 may be transformed—e.g., to holddifferent data.

Aspects of logic subsystem 602 and storage subsystem 604 may beintegrated together into one or more hardware-logic components. Suchhardware-logic components may include program- and application-specificintegrated circuits (PASIC/ASICs), program- and application-specificstandard products (PSSP/ASSPs), system-on-a-chip (SOC), and complexprogrammable logic devices (CPLDs), for example.

The logic subsystem and the storage subsystem may cooperate toinstantiate one or more logic machines. As used herein, the term“machine” is used to collectively refer to the combination of hardware,firmware, software, instructions, and/or any other componentscooperating to provide computer functionality. In other words,“machines” are never abstract ideas and always have a tangible form. Amachine may be instantiated by a single computing device, or a machinemay include two or more sub-components instantiated by two or moredifferent computing devices. In some implementations a machine includesa local component (e.g., software application executed by a computerprocessor) cooperating with a remote component (e.g., cloud computingservice provided by a network of server computers). The software and/orother instructions that give a particular machine its functionality mayoptionally be saved as one or more unexecuted modules on one or moresuitable storage devices. For example, the previously-trainedcode-generation machine, previously-trained relevance detection machine,and/or error-handling execution machine are examples of machinesaccording to the present disclosure.

Machines may be implemented using any suitable combination ofstate-of-the-art and/or future machine learning (ML), artificialintelligence (AI), and/or natural language processing (NLP) techniques.For example, the previously-trained code-generation machine and/orpreviously-trained relevance detection machine may incorporate anysuitable ML, AI, and/or NLP techniques, including any suitable languagemodels.

Non-limiting examples of techniques that may be incorporated in animplementation of one or more machines include support vector machines,multi-layer neural networks, convolutional neural networks (e.g.,including spatial convolutional networks for processing images and/orvideos, temporal convolutional neural networks for processing audiosignals and/or natural language sentences, and/or any other suitableconvolutional neural networks configured to convolve and pool featuresacross one or more temporal and/or spatial dimensions), recurrent neuralnetworks (e.g., long short-term memory networks), associative memories(e.g., lookup tables, hash tables, Bloom Filters, Neural Turing Machineand/or Neural Random Access Memory), word embedding models (e.g., GloVeor Word2Vec), unsupervised spatial and/or clustering methods (e.g.,nearest neighbor algorithms, topological data analysis, and/or k-meansclustering), graphical models (e.g., (hidden) Markov models, Markovrandom fields, (hidden) conditional random fields, and/or AI knowledgebases), and/or natural language processing techniques (e.g.,tokenization, stemming, constituency and/or dependency parsing, and/orintent recognition, segmental models, and/or super-segmental models(e.g., hidden dynamic models)).

In some examples, the methods and processes described herein may beimplemented using one or more differentiable functions, wherein agradient of the differentiable functions may be calculated and/orestimated with regard to inputs and/or outputs of the differentiablefunctions (e.g., with regard to training data, and/or with regard to anobjective function). Such methods and processes may be at leastpartially determined by a set of trainable parameters. Accordingly, thetrainable parameters for a particular method or process may be adjustedthrough any suitable training procedure, in order to continually improvefunctioning of the method or process.

Non-limiting examples of training procedures for adjusting trainableparameters include supervised training (e.g., using gradient descent orany other suitable optimization method), zero-shot, few-shot,unsupervised learning methods (e.g., classification based on classesderived from unsupervised clustering methods), reinforcement learning(e.g., deep Q learning based on feedback) and/or generative adversarialneural network training methods, belief propagation, RANSAC (randomsample consensus), contextual bandit methods, maximum likelihoodmethods, and/or expectation maximization. In some examples, a pluralityof methods, processes, and/or components of systems described herein maybe trained simultaneously with regard to an objective function measuringperformance of collective functioning of the plurality of components(e.g., with regard to reinforcement feedback and/or with regard tolabelled training data). Simultaneously training the plurality ofmethods, processes, and/or components may improve such collectivefunctioning. In some examples, one or more methods, processes, and/orcomponents may be trained independently of other components (e.g.,offline training on historical data).

The previously-trained code-generation machine and/or previously-trainedrelevance detection machine may incorporate any suitable languagemodels. Language models may utilize vocabulary features to guidesampling/searching for words for recognition of speech. For example, alanguage model may be at least partially defined by a statisticaldistribution of words or other vocabulary features. For example, alanguage model may be defined by a statistical distribution of n-grams,defining transition probabilities between candidate words according tovocabulary statistics. The language model may be further based on anyother appropriate statistical features, and/or results of processing thestatistical features with one or more machine learning and/orstatistical algorithms (e.g., confidence values resulting from suchprocessing). In some examples, a statistical model may constrain whatwords may be recognized for an audio signal, e.g., based on anassumption that words in the audio signal come from a particularvocabulary.

Alternately or additionally, the language model may be based on one ormore neural networks previously trained to represent audio inputs andwords in a shared latent space, e.g., a vector space learned by one ormore audio and/or word models (e.g., wav2letter and/or word2vec).Accordingly, finding a candidate word may include searching the sharedlatent space based on a vector encoded by the audio model for an audioinput, in order to find a candidate word vector for decoding with theword model. The shared latent space may be utilized to assess, for oneor more candidate words, a confidence that the candidate word isfeatured in the speech.

The language model may be used in conjunction with an acoustical modelconfigured to assess, for a candidate word and an audio signal, aconfidence that the candidate word is included in speech in the audiosignal based on acoustical features of the word (e.g., mel-frequencycepstral coefficients, formants, etc.). Optionally, in some examples,the language model may incorporate the acoustical model (e.g.,assessment and/or training of the language model may be based on theacoustical model). The acoustical model defines a mapping betweenacoustic signals and basic sound units such as phonemes, e.g., based onlabelled speech. The acoustical model may be based on any suitablecombination of state-of-the-art or future machine learning (ML) and/orartificial intelligence (AI) models, for example: deep neural networks(e.g., long short-term memory, temporal convolutional neural network,restricted Boltzmann machine, deep belief network), hidden Markov models(HMM), conditional random fields (CRF) and/or Markov random fields,Gaussian mixture models, and/or other graphical models (e.g., deepBayesian network). Audio signals to be processed with the acoustic modelmay be pre-processed in any suitable manner, e.g., encoding at anysuitable sampling rate, Fourier transform, band-pass filters, etc. Theacoustical model may be trained to recognize the mapping betweenacoustic signals and sound units based on training with labelled audiodata. For example, the acoustical model may be trained based on labelledaudio data comprising speech and corrected text, in order to learn themapping between the speech signals and sound units denoted by thecorrected text. Accordingly, the acoustical model may be continuallyimproved to improve its utility for correctly recognizing speech.

In some examples, in addition to statistical models, neural networks,and/or acoustical models, the language model may incorporate anysuitable graphical model, e.g., a hidden Markov model (HMM) or aconditional random field (CRF). The graphical model may utilizestatistical features (e.g., transition probabilities) and/or confidencevalues to determine a probability of recognizing a word, given thespeech and/or other words recognized so far. Accordingly, the graphicalmodel may utilize the statistical features, previously trained machinelearning models, and/or acoustical models to define transitionprobabilities between states represented in the graphical model.

When included, input/output subsystem 606 may comprise one or moredisplays, which may be used to present a visual representation of dataheld by storage subsystem 604. This visual representation may take theform of a graphical user interface (GUI). Input/output subsystem 606 mayinclude one or more display devices utilizing virtually any type oftechnology. In some implementations, display subsystem may include oneor more virtual-, augmented-, or mixed reality displays. When included,input/output subsystem 606 may further comprise one or more speakersconfigured to output speech, e.g., to present an audible representationof data held by storage subsystem 604, such as automated assistantresponses.

When included, input/output subsystem 606 may comprise or interface withone or more input devices. An input device may include a sensor deviceor a user input device. Examples of user input devices include akeyboard, mouse, touch screen, or game controller. In some embodiments,the input subsystem may comprise or interface with selected natural userinput (NUI) componentry. Such componentry may be integrated orperipheral, and the transduction and/or processing of input actions maybe handled on- or off-board. Example NUI componentry may include amicrophone for speech and/or voice recognition; an infrared, color,stereoscopic, and/or depth camera for machine vision and/or gesturerecognition; a head tracker, eye tracker, accelerometer, and/orgyroscope for motion detection and/or intent recognition.

When included, communication subsystem 608 may be configured tocommunicatively couple computing system 600 with one or more othercomputing devices. Communication subsystem 608 may include wired and/orwireless communication devices compatible with one or more differentcommunication protocols. The communication subsystem may be configuredfor communication via personal-, local- and/or wide-area networks.

This disclosure is presented by way of example and with reference to theassociated drawing figures. Components, process steps, and otherelements that may be substantially the same in one or more of thefigures are identified coordinately and are described with minimalrepetition. It will be noted, however, that elements identifiedcoordinately may also differ to some degree. It will be further notedthat some figures may be schematic and not drawn to scale. The variousdrawing scales, aspect ratios, and numbers of components shown in thefigures may be purposely distorted to make certain features orrelationships easier to see.

In an example, a method comprises: recognizing a user utterance; using apreviously-trained code-generation machine to produce, from the userutterance, a data-flow program that defines an executable plan forresponding to the user utterance; and executing the data-flow program.

In an example, a method comprises: recognizing a user utteranceincluding an ambiguity; using a previously-trained code-generationmachine to produce, from the user utterance, a data-flow programincluding a search-history function; wherein the previously-trained codegeneration machine is configured to add any of a plurality ofpre-defined composable functions to the data-flow program based on theuser utterance including the ambiguity; and wherein the search-historyfunction is configured to select a highest-confidence disambiguatingconcept from one or more candidate concepts stored in a context-specificdialogue history.

In an example, a method comprises: recognizing a user utteranceincluding an ambiguity; using a previously-trained code-generationmachine to produce, from the user utterance, a data-flow programincluding a search-history function; wherein the search-history functionis configured to select a highest-confidence disambiguating concept fromone or more candidate concepts stored in a context-specific dialoguehistory; and the method further comprises recognizing a constraintrelated to the ambiguity, wherein the search-history function isconfigured to use a previously-trained relevance detection machine toselect a disambiguating data-flow program fragment corresponding to thedisambiguating concept from the context-specific dialogue history basedon such recognized constraint.

In an example, a method comprises: recognizing a user utteranceincluding an ambiguity; using a previously-trained code-generationmachine to produce, from the user utterance, a data-flow programincluding a search-history function; wherein the search-history functionis configured to select a highest-confidence disambiguating concept fromone or more candidate concepts stored in a context-specific dialoguehistory. In this or any other example, the method further comprisesrecognizing a constraint related to the ambiguity, wherein thesearch-history function is configured to search, in the context-specificdialogue history, for a subset of the one or more candidate conceptssatisfying the constraint related to the ambiguity, and wherein thehighest-confidence disambiguating concept is one of the subset ofcandidate concepts. In this or any other example, the constraintindicates an ambiguous entity and an entity property of the ambiguousentity, and wherein the subset of the one or more candidate conceptsincludes candidate entities from the context-specific dialogue historyhaving the entity property. In this or any other example, the constraintindicates an ambiguous reference to an action performed by an automatedassistant and a constraining property related to the action, and whereinthe subset of the one or more candidate concepts includes a plurality ofcandidate actions having the constraining property, wherein eachcandidate action is defined by a candidate data-flow program fragment.In this or any other example, the method further comprises recognizing aconstraint related to the ambiguity, wherein the search-history functionis configured to use a previously-trained relevance detection machine toselect a disambiguating data-flow program fragment corresponding to thedisambiguating concept from the context-specific dialogue history basedon such recognized constraint. In this or any other example, thepreviously-trained relevance detection machine is trained via supervisedtraining on a plurality of annotated dialogue histories, wherein anannotated dialogue history includes an unresolved search-historyfunction labeled with a disambiguating concept that would resolve theunresolved search-history function. In this or any other example, thedisambiguating concept that would resolve the unresolved search-historyfunction is selected by a human demonstrator from the context-specificdialogue history. In this or any other example, the exemplarydisambiguating concept for the exemplary search-history functionincludes an exemplary program fragment received from a humandemonstrator. In this or any other example, the data-flow program isconfigured for unconditional evaluation of one or more data valuesincluding a return value, and the method further includes executing thedata-flow program and, responsive to reaching any error condition whileexecution the data-flow program: suspending execution of the data-flowprogram; using the previously-trained code-generation machine togenerate an error-handling data-flow program based on the suspendedexecution of the data-flow program; and resuming execution with theerror-handling data-flow program. In this or any other example, thepreviously-trained code generation machine is configured to add any of aplurality of pre-defined composable functions to the data-flow programbased on the user utterance including the ambiguity. In this or anyother example, the plurality of pre-defined composable functionsincludes an intelligent decision function, wherein the intelligentdecision function is configured to use a previously-trained machinelearning model to perform a calculation. In this or any other example,the plurality of pre-defined composable functions includes auser-customized function configured to access a user customizationsetting and to perform a calculation based on the user customizationsetting. In this or any other example, the plurality of pre-definedcomposable functions includes a foreign function configured to invoke aforeign application programming interface (API). In this or any otherexample, the plurality of pre-defined composable functions includes aninference function configured to perform a calculation with regard to aresult of the foreign function. In this or any other example, theplurality of pre-defined composable functions includes a macro function,wherein the macro function includes a plurality of other pre-definedcomposable functions and wherein the macro function is configured toexecute the plurality of other pre-defined composable functions. In thisor any other example, the plurality of pre-defined composable functionsincludes a program rewriting function, parametrized by a designatedconcept stored in the context-specific dialogue history, and configuredto generate a new data-flow program fragment related to the designatedconcept. In this or any other example, the designated concept stored inthe context-specific dialogue history includes a target sub-concept,wherein the program rewriting function is further parametrized by areplacing sub-concept, and wherein the new data-flow program fragmentcorresponds to the designated concept with the target sub-concept beingreplaced by the replacing sub-concept. In this or any other example, thepreviously-trained code generation machine is trained via supervisedtraining on a plurality of annotated dialogue histories, wherein anannotated dialogue history includes an exemplary user utterance and anexemplary data-flow program including the search-history function.

In an example, a computer system comprises: a microphone; a logicdevice; and a storage device holding instructions executable by thelogic device to: receive speech sounds from the microphone; recognize,in the speech sounds, a user utterance including an ambiguity; and use apreviously-trained code-generation machine to produce, from the userutterance, a data-flow program including a search-history function,wherein the search-history function is configured to select ahighest-confidence disambiguating concept from one or more candidateconcepts stored in a context-specific dialogue history.

In an example, a method comprises: recognizing a user utteranceincluding an ambiguity; recognizing a constraint related to theambiguity; using a previously-trained code-generation machine toproduce, from the user utterance, a data-flow program including asearch-history function, wherein the search-history function isconfigured to: search one or more candidate concepts stored in acontext-specific dialogue history for a subset of the one or morecandidate concepts satisfying the constraint related to the ambiguity;use a previously-trained relevance detection machine to select ahighest-confidence disambiguating concept from the subset of the one ormore candidate concepts; and select a disambiguating data-flow programfragment corresponding to the disambiguating concept.

It will be understood that the configurations and/or approachesdescribed herein are exemplary in nature, and that these specificembodiments or examples are not to be considered in a limiting sense,because numerous variations are possible. The specific routines ormethods described herein may represent one or more of any number ofprocessing strategies. As such, various acts illustrated and/ordescribed may be performed in the sequence illustrated and/or described,in other sequences, in parallel, or omitted. Likewise, the order of theabove-described processes may be changed.

The invention claimed is:
 1. A method, comprising: recognizing a userutterance including an ambiguity; using a previously-trainedcode-generation machine to produce, from the user utterance, a data-flowprogram including a search-history function; wherein the search-historyfunction is configured to select a highest-confidence disambiguatingconcept from one or more candidate concepts stored in a context-specificdialogue history.
 2. The method of claim 1, further comprisingrecognizing a constraint related to the ambiguity, wherein thesearch-history function is configured to search, in the context-specificdialogue history, for a subset of the one or more candidate conceptssatisfying the constraint related to the ambiguity, and wherein thehighest-confidence disambiguating concept is one of the subset ofcandidate concepts.
 3. The method of claim 2, wherein the constraintindicates an ambiguous entity and an entity property of the ambiguousentity, and wherein the subset of the one or more candidate conceptsincludes candidate entities from the context-specific dialogue historyhaving the entity property.
 4. The method of claim 2, wherein theconstraint indicates an ambiguous reference to an action performed by anautomated assistant and a constraining property related to the action,and wherein the subset of the one or more candidate concepts includes aplurality of candidate actions having the constraining property, whereineach candidate action is defined by a candidate data-flow programfragment.
 5. The method of claim 1, further comprising recognizing aconstraint related to the ambiguity, wherein the search-history functionis configured to use a previously-trained relevance detection machine toselect a disambiguating data-flow program fragment corresponding to thedisambiguating concept from the context-specific dialogue history basedon such recognized constraint.
 6. The method of claim 5, wherein thepreviously-trained relevance detection machine is trained via supervisedtraining on a plurality of annotated dialogue histories, wherein anannotated dialogue history includes an unresolved search-historyfunction labeled with a disambiguating concept that would resolve theunresolved search-history function.
 7. The method of claim 6, whereinthe disambiguating concept that would resolve the unresolvedsearch-history function is selected by a human demonstrator from thecontext-specific dialogue history.
 8. The method of claim 6, wherein theexemplary disambiguating concept for the exemplary search-historyfunction includes an exemplary program fragment received from a humandemonstrator.
 9. The method of claim 1, wherein the data-flow program isconfigured for unconditional evaluation of one or more data valuesincluding a return value, and the method further comprises executing thedata-flow program and, responsive to reaching any error condition whileexecution the data-flow program: suspending execution of the data-flowprogram; using the previously-trained code-generation machine togenerate an error-handling data-flow program based on the suspendedexecution of the data-flow program; and resuming execution with theerror-handling data-flow program.
 10. The method of claim 1, wherein thepreviously-trained code generation machine is configured to add any of aplurality of pre-defined composable functions to the data-flow programbased on the user utterance including the ambiguity.
 11. The method ofclaim 10, wherein the plurality of pre-defined composable functionsincludes an intelligent decision function, wherein the intelligentdecision function is configured to use a previously-trained machinelearning model to perform a calculation.
 12. The method of claim 10,wherein the plurality of pre-defined composable functions includes auser-customized function configured to access a user customizationsetting and to perform a calculation based on the user customizationsetting.
 13. The method of claim 10, wherein the plurality ofpre-defined composable functions includes a foreign function configuredto invoke a foreign application programming interface (API).
 14. Themethod of claim 13, wherein the plurality of pre-defined composablefunctions includes an inference function configured to perform acalculation with regard to a result of the foreign function.
 15. Themethod of claim 10, wherein the plurality of pre-defined composablefunctions includes a macro function, wherein the macro function includesa plurality of other pre-defined composable functions and wherein themacro function is configured to execute the plurality of otherpre-defined composable functions.
 16. The method of claim 10, whereinthe plurality of pre-defined composable functions includes a programrewriting function, parametrized by a designated concept stored in thecontext-specific dialogue history, and configured to generate a newdata-flow program fragment related to the designated concept.
 17. Themethod of claim 16, wherein the designated concept stored in thecontext-specific dialogue history includes a target sub-concept, whereinthe program rewriting function is further parametrized by a replacingsub-concept, and wherein the new data-flow program fragment correspondsto the designated concept with the target sub-concept being replaced bythe replacing sub-concept.
 18. The method of claim 1, wherein thepreviously-trained code generation machine is trained via supervisedtraining on a plurality of annotated dialogue histories, wherein anannotated dialogue history includes an exemplary user utterance and anexemplary data-flow program including the search-history function.
 19. Acomputer system, comprising: a microphone; a logic device; and a storagedevice holding instructions executable by the logic device to: receivespeech sounds from the microphone; recognize, in the speech sounds, auser utterance including an ambiguity; and use a previously-trainedcode-generation machine to produce, from the user utterance, a data-flowprogram including a search-history function, wherein the search-historyfunction is configured to select a highest-confidence disambiguatingconcept from one or more candidate concepts stored in a context-specificdialogue history.
 20. A method, comprising: recognizing a user utteranceincluding an ambiguity; recognizing a constraint related to theambiguity; using a previously-trained code-generation machine toproduce, from the user utterance, a data-flow program including asearch-history function, wherein the search-history function isconfigured to: search one or more candidate concepts stored in acontext-specific dialogue history for a subset of the one or morecandidate concepts satisfying the constraint related to the ambiguity;use a previously-trained relevance detection machine to select ahighest-disambiguating concept from the subset of the one or morecandidate concepts; and select a disambiguating data-flow programfragment corresponding to the disambiguating concept.